{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:06Z","timestamp":1750220046395,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001858","name":"Vinnova","doi-asserted-by":"publisher","award":["2018-05012"],"award-info":[{"award-number":["2018-05012"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,17]]},"DOI":"10.1145\/3587716.3587758","type":"proceedings-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T23:27:30Z","timestamp":1694129250000},"page":"252-258","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Surrogate Deep Learning to Estimate Uncertainties for Driver Intention Recognition"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2135-6615","authenticated-orcid":false,"given":"Koen","family":"Vellenga","sequence":"first","affiliation":[{"name":"Sk\u00f6vde Artificial Intelligence Lab, University of Sk\u00f6vde, Sweden and R&amp;D Analytics &amp; AI CoE, Volvo Car Corporation, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2973-3112","authenticated-orcid":false,"given":"Alexander","family":"Karlsson","sequence":"additional","affiliation":[{"name":"Sk\u00f6vde Artificial Intelligence Lab, University of Sk\u00f6vde, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2949-4123","authenticated-orcid":false,"given":"H. Joe","family":"Steinhauer","sequence":"additional","affiliation":[{"name":"Sk\u00f6vde Artificial Intelligence Lab, University of Sk\u00f6vde, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8884-2154","authenticated-orcid":false,"given":"G\u00f6ran","family":"Falkman","sequence":"additional","affiliation":[{"name":"Sk\u00f6vde Artificial Intelligence Lab, University of Sk\u00f6vde, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2579-7063","authenticated-orcid":false,"given":"Anders","family":"Sjogren","sequence":"additional","affiliation":[{"name":"R&amp;D Analytics &amp; AI CoE, Volvo Car Corporation, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg Corrado Andy Davis Jeffrey Dean Matthieu Devin 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. (2015)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982170"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR, 1613\u20131622","author":"Blundell Charles","year":"2015","unstructured":"Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. 2015. Weight uncertainty in neural network. In International Conference on Machine Learning. PMLR, 1613\u20131622."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2991\/jase.d.190131.001"},{"key":"e_1_3_2_1_5_1","volume-title":"Dataset shift in machine learning","author":"Candela J\u00a0Qui\u00f1onero","year":"2009","unstructured":"J\u00a0Qui\u00f1onero Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil\u00a0D Lawrence. 2009. Dataset shift in machine learning. The MIT Press 1 (2009), 5."},{"key":"e_1_3_2_1_6_1","unstructured":"E Commission 2021. Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. COM (2021) 206 (2021)."},{"key":"e_1_3_2_1_7_1","unstructured":"Michael\u00a0C Darling. 2019. Using Uncertainty To Interpret Supervised Machine Learning Predictions. (2019)."},{"key":"e_1_3_2_1_8_1","volume-title":"Aleatory or epistemic? Does it matter?Structural safety 31, 2","author":"Der\u00a0Kiureghian Armen","year":"2009","unstructured":"Armen Der\u00a0Kiureghian and Ove Ditlevsen. 2009. Aleatory or epistemic? Does it matter?Structural safety 31, 2 (2009), 105\u2013112."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1964.10490181"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3322706.3361996"},{"key":"e_1_3_2_1_11_1","volume-title":"Human-centered autonomous vehicle systems: Principles of effective shared autonomy. arXiv preprint arXiv:1810.01835","author":"Fridman Lex","year":"2018","unstructured":"Lex Fridman. 2018. Human-centered autonomous vehicle systems: Principles of effective shared autonomy. arXiv preprint arXiv:1810.01835 (2018)."},{"key":"e_1_3_2_1_12_1","volume-title":"international conference on machine learning. PMLR, 1050\u20131059","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning. PMLR, 1050\u20131059."},{"key":"e_1_3_2_1_13_1","volume-title":"Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel","author":"Gawlikowski Jakob","year":"2021","unstructured":"Jakob Gawlikowski, Cedrique Rovile\u00a0Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, 2021. A survey of uncertainty in deep neural networks. arXiv preprint arXiv:2107.03342 (2021)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"e_1_3_2_1_15_1","volume-title":"Practical variational inference for neural networks. Advances in neural information processing systems 24","author":"Graves Alex","year":"2011","unstructured":"Alex Graves. 2011. Practical variational inference for neural networks. Advances in neural information processing systems 24 (2011)."},{"key":"e_1_3_2_1_16_1","volume-title":"Attention-Based GRU for Driver Intention Recognition and Vehicle Trajectory Prediction. In 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 86\u201391","author":"Hao Zixu","year":"2020","unstructured":"Zixu Hao, Xing Huang, Kaige Wang, Maoyuan Cui, and Yantao Tian. 2020. Attention-Based GRU for Driver Intention Recognition and Vehicle Trajectory Prediction. In 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 86\u201391."},{"key":"e_1_3_2_1_17_1","volume-title":"Unsolved problems in ML safety. arXiv preprint arXiv:2109.13916","author":"Hendrycks Dan","year":"2021","unstructured":"Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. 2021. Unsolved problems in ML safety. arXiv preprint arXiv:2109.13916 (2021)."},{"key":"e_1_3_2_1_18_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735\u20131780."},{"key":"e_1_3_2_1_19_1","volume-title":"A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics","author":"Holm Sture","year":"1979","unstructured":"Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics (1979), 65\u201370."},{"key":"e_1_3_2_1_20_1","unstructured":"Google Inc.2022. Tensorflow probability."},{"key":"e_1_3_2_1_21_1","volume-title":"34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. Association For Uncertainty in Artificial Intelligence (AUAI), 876\u2013885","author":"Izmailov Pavel","year":"2018","unstructured":"Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew\u00a0Gordon Wilson. 2018. Averaging weights leads to wider optima and better generalization. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. Association For Uncertainty in Artificial Intelligence (AUAI), 876\u2013885."},{"key":"e_1_3_2_1_22_1","volume-title":"International conference on machine learning. PMLR, 4629\u20134640","author":"Izmailov Pavel","year":"2021","unstructured":"Pavel Izmailov, Sharad Vikram, Matthew\u00a0D Hoffman, and Andrew Gordon\u00a0Gordon Wilson. 2021. What are Bayesian neural network posteriors really like?. In International conference on machine learning. PMLR, 4629\u20134640."},{"key":"e_1_3_2_1_23_1","unstructured":"Ashesh Jain Hema\u00a0S Koppula Shane Soh Bharad Raghavan Avi Singh and Ashutosh Saxena. 2016. Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture. (2016). arXiv:1601.00740v1\u00a0[cs.RO]"},{"key":"e_1_3_2_1_24_1","volume-title":"What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems 30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_25_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_26_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma P","year":"2013","unstructured":"Diederik\u00a0P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1952.10483441"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2019.106816"},{"key":"e_1_3_2_1_29_1","volume-title":"Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30","author":"Lakshminarayanan Balaji","year":"2017","unstructured":"Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_30_1","first-page":"13153","article-title":"A simple baseline for bayesian uncertainty in deep learning","volume":"32","author":"Maddox J","year":"2019","unstructured":"Wesley\u00a0J Maddox, Pavel Izmailov, Timur Garipov, Dmitry\u00a0P Vetrov, and Andrew\u00a0Gordon Wilson. 2019. A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems 32 (2019), 13153\u201313164.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01290"},{"key":"e_1_3_2_1_32_1","volume-title":"On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics","author":"Mann B","year":"1947","unstructured":"Henry\u00a0B Mann and Donald\u00a0R Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics (1947), 50\u201360."},{"key":"e_1_3_2_1_33_1","volume-title":"Bayesian learning via stochastic dynamics. Advances in neural information processing systems 5","author":"Neal Radford","year":"1992","unstructured":"Radford Neal. 1992. Bayesian learning via stochastic dynamics. Advances in neural information processing systems 5 (1992)."},{"key":"e_1_3_2_1_34_1","volume-title":"MCMC using Hamiltonian dynamics. Handbook of markov chain monte carlo 2, 11","author":"M Neal","year":"2011","unstructured":"Radford\u00a0M Neal 2011. MCMC using Hamiltonian dynamics. Handbook of markov chain monte carlo 2, 11 (2011), 2."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2004.03.002"},{"key":"e_1_3_2_1_36_1","first-page":"13991","article-title":"Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift","volume":"32","author":"Ovadia Yaniv","year":"2019","unstructured":"Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019. Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. Advances in Neural Information Processing Systems 32 (2019), 13991\u201314002.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3233\/AIC-130559"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00023"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097)","author":"Raghu Maithra","year":"2019","unstructured":"Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, and Jon Kleinberg. 2019. Direct Uncertainty Prediction for Medical Second Opinions. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 5281\u20135290. https:\/\/proceedings.mlr.press\/v97\/raghu19a.html"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-62144-5_7"},{"key":"e_1_3_2_1_41_1","volume-title":"International conference on learning representations.","author":"Riquelme Carlos","year":"2018","unstructured":"Carlos Riquelme, George Tucker, and Jasper Snoek. 2018. Deep bayesian bandits showdown. In International conference on learning representations."},{"key":"e_1_3_2_1_42_1","volume-title":"Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1\u20138.","author":"Rong Yao","year":"2020","unstructured":"Yao Rong, Zeynep Akata, and Enkelejda Kasneci. 2020. Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1\u20138."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0048-x"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Fariba Sadri. 2011. Logic-based approaches to intention recognition. In Handbook of research on ambient intelligence and smart environments: Trends and perspectives. IGI Global 346\u2013375.","DOI":"10.4018\/978-1-61692-857-5.ch018"},{"key":"e_1_3_2_1_45_1","volume-title":"Kernelized convolutional transformer network based driver behavior estimation for conflict resolution at unsignalized roundabout. ISA transactions","author":"Sharma Omveer","year":"2022","unstructured":"Omveer Sharma, NC Sahoo, and Niladri\u00a0B Puhan. 2022. Kernelized convolutional transformer network based driver behavior estimation for conflict resolution at unsignalized roundabout. ISA transactions (2022)."},{"key":"e_1_3_2_1_46_1","volume-title":"Deep learning in medical image analysis. Annual review of biomedical engineering 19","author":"Shen Dinggang","year":"2017","unstructured":"Dinggang Shen, Guorong Wu, and Heung-Il Suk. 2017. Deep learning in medical image analysis. Annual review of biomedical engineering 19 (2017), 221."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"key":"e_1_3_2_1_48_1","volume-title":"International conference on machine learning. PMLR, 2171\u20132180","author":"Snoek Jasper","year":"2015","unstructured":"Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams. 2015. Scalable bayesian optimization using deep neural networks. In International conference on machine learning. PMLR, 2171\u20132180."},{"key":"e_1_3_2_1_49_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929\u20131958."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50146-4_41"},{"key":"e_1_3_2_1_51_1","volume-title":"Statistical consequences of fat tails: Real world preasymptotics, epistemology, and applications. arXiv preprint arXiv:2001.10488","author":"Taleb Nassim\u00a0Nicholas","year":"2020","unstructured":"Nassim\u00a0Nicholas Taleb. 2020. Statistical consequences of fat tails: Real world preasymptotics, epistemology, and applications. arXiv preprint arXiv:2001.10488 (2020)."},{"key":"e_1_3_2_1_52_1","volume-title":"Edward: A library for probabilistic modeling, inference, and criticism. arXiv preprint arXiv:1610.09787","author":"Tran Dustin","year":"2016","unstructured":"Dustin Tran, Alp Kucukelbir, Adji\u00a0B. Dieng, Maja Rudolph, Dawen Liang, and David\u00a0M. Blei. 2016. Edward: A library for probabilistic modeling, inference, and criticism. arXiv preprint arXiv:1610.09787 (2016)."},{"key":"e_1_3_2_1_53_1","volume-title":"Alphax: exploring neural architectures with deep neural networks and monte carlo tree search. arXiv preprint arXiv:1903.11059","author":"Wang Linnan","year":"2019","unstructured":"Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, and Rodrigo Fonseca. 2019. Alphax: exploring neural architectures with deep neural networks and monte carlo tree search. arXiv preprint arXiv:1903.11059 (2019)."},{"key":"e_1_3_2_1_54_1","volume-title":"Latent Derivative Bayesian Last Layer Networks. In International Conference on Artificial Intelligence and Statistics. PMLR, 1198\u20131206","author":"Watson Joe","year":"2021","unstructured":"Joe Watson, Jihao\u00a0Andreas Lin, Pascal Klink, Joni Pajarinen, and Jan Peters. 2021. Latent Derivative Bayesian Last Layer Networks. In International Conference on Artificial Intelligence and Statistics. PMLR, 1198\u20131206."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-55583-2_25"},{"key":"e_1_3_2_1_56_1","volume-title":"Bayesian deep learning and a probabilistic perspective of generalization. Advances in neural information processing systems 33","author":"Wilson G","year":"2020","unstructured":"Andrew\u00a0G Wilson and Pavel Izmailov. 2020. Bayesian deep learning and a probabilistic perspective of generalization. Advances in neural information processing systems 33 (2020), 4697\u20134708."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102615"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_1_59_1","volume-title":"Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1275\u20131282","author":"Zhou Weitao","year":"2022","unstructured":"Weitao Zhou, Zhong Cao, Yunkang Xu, Nanshan Deng, Xiaoyu Liu, Kun Jiang, and Diange Yang. 2022. Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1275\u20131282."},{"key":"e_1_3_2_1_60_1","volume-title":"A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications. Neurocomputing","author":"Zhou Xinlei","year":"2021","unstructured":"Xinlei Zhou, Han Liu, Farhad Pourpanah, Tieyong Zeng, and Xizhao Wang. 2021. A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications. Neurocomputing (2021)."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.05.024"}],"event":{"name":"ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing","acronym":"ICMLC 2023","location":"Zhuhai China"},"container-title":["Proceedings of the 2023 15th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587758","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3587716.3587758","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:00Z","timestamp":1750183680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":61,"alternative-id":["10.1145\/3587716.3587758","10.1145\/3587716"],"URL":"https:\/\/doi.org\/10.1145\/3587716.3587758","relation":{},"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"2023-09-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}